AI Insight
This study evaluated whether pseudo-monopolar beta-band brain activity recordings from the subthalamic nucleus can predict optimal electrode contacts for deep brain stimulation in Parkinson's Disease patients. Testing 69 patients, researchers found that the Electrode Identifier feature correctly predicted clinically selected contacts 45% of the time for the top choice and 57% for broader criteria, performing comparably to traditional bipolar recording methods. However, neither method reliably predicted motor outcome improvements, and technical limitations excluded one-third of recordings from analysis.
Why it matters
Selecting optimal stimulation contacts for deep brain stimulation is currently time-intensive and relies heavily on clinical trial-and-error. A biomarker-guided approach using beta-band activity could potentially streamline this process, reduce patient burden during programming sessions, and improve efficiency of DBS therapy management for Parkinson's Disease.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson’s Disease (PD), but identifying optimal stimulation contacts is time-intensive. Beta-band activity (13-35 Hz) from local field potentials (LFP) correlates with motor symptoms and attenuation by dopaminergic therapy and DBS supports its role as a programming biomarker. The recently introduced Electrode Identifier (EI) feature of Medtronic PerceptTM neurostimulators may facilitate beta-guided contact selection. Objective: To evaluate whether pseudo-monopolar STN beta power obtained using EI predicts optimal stimulation contacts and compare its performance with reconstructed bipolar recordings and MPR. Methods: LFPs were recorded in 69 patients using EI and Electrode Survey (ES). Prediction accuracy was assessed using predefined ranking rules and compared with clinically selected contacts. Agreement between EI, ES, and MPR was evaluated. Motor outcome was assessed using MDS-UPDRS-III. Results: EI predicted clinically selected contacts above chance (TOP1: 45%, p = 0.010; TOP2-80: 57%, p = <0.001), whereas ES exceeded chance only under more inclusive selection criteria (TOP1: 38%, p = 0.073; TOP2-80: 55%, p = 0.0021). Accuracy did not differ between methods (TOP1: p = 0.720; TOP2-80: p = 1.000). EI showed highest agreement with MPR and tended to select ventral contacts. Neither method predicted motor outcome, although EI-matched contacts showed a trend toward greater improvement. Due to technical constraints, one-third of EI recordings were excluded. Conclusions: Pseudo-monopolar STN beta power provides clinically relevant information for DBS contact selection with performance comparable to bipolar approaches. Further improvements are needed before clinical implementation.